Method and system for automatic zone axis alignment

ABSTRACT

Automatic alignment of the zone axis of a sample and a charged particle beam is achieved based on a diffraction pattern of the sample. An area corresponding to the Laue circle is segmented using a trained network. The sample is aligned with the charged particle beam by tilting the sample with a zone axis tilt determined based on the segmented area.

This application is a continuation application under 35 U.S.C. § 120 ofpending U.S. application Ser. No. 16/730,998, filed Dec. 30, 2019. Theentire contents of the aforementioned applications are incorporated byreference herein.

FIELD OF THE INVENTION

The present description relates generally to methods and systems foraligning a sample with an incident beam, and more particularly, toautomatically aligning a zone axis of a crystalline sample with acharged particle beam.

BACKGROUND OF THE INVENTION

For high resolution charged particle beam microscopy, in order to imagea crystalline sample with high accuracy, the charged particle beam hasto be aligned with a zone axis of the crystalline sample. If the zoneaxis of the sample is misaligned, such as when the zone axis is notoriented parallel to the incident beam, the measurement of the nanoscalefeatures on the sample may be inaccurate. The process of aligning thesample's crystal structure with the incident beam is referred to as zoneaxis alignment.

One method of zone axis alignment is based on diffraction patternsformed with a parallel beam. When the parallel charged particle beampasses through a thin crystalline sample, the charged particlesinterfere with each other and form a diffraction pattern on the backfocal plane of an objective lens positioned below the sample. Thediffraction pattern consists of multiple bright diffraction spots.Diffraction spots belonging to the zero order Laue circle may bedetermined, and the zone axis misalignment may be determined based onthe locations of the center of the zero order Laue circle and the centerof the direct beam. However, Applicant recognizes that under certainconditions, diffraction spots of the zero order Laue circle cannot beeasily identified. As one example, when the sample is curved, or bendy,diffraction spots of different zone axis orientations may be mixed inthe diffraction pattern. As another example, under irradiation of aconvergent beam, the diffraction spots become disks and may be elongatedand overlapped with each other and/or the direct beam.

SUMMARY

In one embodiment, a zone axis of the sample may be aligned using amethod that comprises directing a charged particle beam towards asample; acquiring a diffraction pattern of the sample; segmenting anarea of the diffraction pattern corresponding to a Laue circle using atrained network; determining a zone axis tilt based on the segmentedarea; and tilting the sample based on the determined zone axis tilt. Inthis way, zone axis of a bendy sample may be automatically aligned basedon diffraction pattern acquired with a convergent beam.

It should be understood that the summary above is provided to introduce,in simplified form, a selection of concepts that are further describedin the detailed description. It is not meant to identify key oressential features of the claimed subject matter, the scope of which isdefined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system for imaging a sample, according to someembodiments.

FIGS. 2A and 2B illustrate relationship between the incident beam andthe sample.

FIG. 3 is a high-level flowchart of a method for zone axis alignment.

FIG. 4 illustrates the coordinate system for adjusting the sampleorientation.

FIG. 5A is an example diffraction pattern acquired with convergent beam.

FIG. 5B shows the output of the trained network.

FIG. 5C shows locations of the Laue circle center and the direct beamcenter.

FIG. 5D is the diffraction pattern of the sample with zone axis alignedwith the incident beam.

FIG. 6 is a flowchart of a method for training a network.

FIG. 7A shows an example annotated diffraction pattern.

FIG. 7B shows an example output of the network.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description relates to systems and methods for aligningthe zone axis of a sample with a charged particle beam. In order toperform high resolution imaging, such as high-resolution scanningtransmission electron microscopy (STEM) imaging using imaging system ofFIG. 1, the zone axis of the crystalline sample has to be parallel withthe incident beam. In STEM, responsive to the charged particlesirradiated on one side of the sample, charged particles including thetransmitted charged particles, scattered electrons and second electronsare detected on the other side of the sample via a detector.

The sample imaged under the transmission mode may bend due to reducedsample thickness or poor mechanical support. As one example, the samplemay have a curvature greater than 5 degrees within a small area of 2um². As shown in FIG. 2A, because sample 202 under irradiation ofparallel beam 201 is not flat, diffraction spots from different crystalorientations may be mixed in the diffraction pattern. As a result,aligning the charged particle beam with one orientation among themultiple crystal orientations based on the diffraction pattern isdifficult. In the contrast, due to its small beam profile on the samplesurface, a diffraction pattern acquired with convergent beam 203, asshown in FIG. 2B, is formed from sample with uniform crystalorientation. However, the diffraction pattern formed with the convergentbeam is more complicated than the diffraction pattern formed with theparallel beam. Unlike the point like high intensity diffraction spots inthe parallel diffraction pattern, diffraction spots formed withconvergent beam diffraction pattern are disk like, elongated, andoverlapped with each other and with the direct beam.

FIG. 3 shows a method for zone axis alignment based on the diffractionpattern. The diffraction pattern may be acquired using a convergent beamor a parallel beam. A first area corresponding to the Laue circle and asecond area corresponding to the direct beam in the diffraction patternare segmented from the diffraction pattern using a trained network. Aquality factor, representing quality of the segmentation, is generatedbased on the output of the trained network. In one example, the qualityfactor is generated based on the shape of the segmented areacorresponding to the Laue circle. In another example, the quality factoris generated based on the relative position of the two segmented areas.The zone axis tilt may be determined based on the locations of thesegmented areas. For example, the angles of zone axis tilt in twoorthogonal tilt directions are derived based on the relative position ofthe center of the first segmented area and the center of the secondsegmented area. The sample orientation may be adjusted based on the zoneaxis tilt to align the zone axis of the sample with the incident beam.The sample orientation may be further adjusted till the zone axis tiltconverges to a threshold zone axis tilt. FIG. 4 shows the coordinatesystem for adjusting the sample orientation. FIGS. 5A-5B show exampleresults of the zone axis alignment.

A method for generating the trained network is shown in FIG. 6. Thenetwork may be trained with multiple diffraction patterns acquired bytilting a reference sample at multiple tilt angles. The diffractionpattern of the reference sample may be acquired under the same beamconditions as the sample for imaging. The areas of the Laue circle andthe direct beam in the multiple diffraction patterns are automaticallyannotated based on the known tilt angles. The areas of the Laue circleand the direct beam may each be a circle. The multiple diffractionpatterns and the annotated diffraction patterns are used for trainingthe network. The network outputs two segmented areas corresponding tothe Laue circle and the direction beam in the diffraction pattern. Theparameters of the network may be adjusted based on the similaritybetween the segmented Laue circle and the annotated Laue circle. FIGS.7A-7B show example annotated diffraction pattern and the network output.

Turning to FIG. 1, a STEM system 100 in accordance with an embodiment ofthe disclosure is shown. The STEM system 100 includes an electron source10 that emits charged particles, such as electron beam 11, towards afocusing column 12. The electron beam may generate high energyelectrons, that is, electrons having typical energies of between about10 keV and 1,000 keV. In some embodiments, the focusing column 12 mayinclude one or more of a condenser lens 121, aperture 122, scan coils123, and upper objective lens 124. The focusing column 12 focuseselectrons from electron source 10 into a small spot on sample 14.Different locations of the sample may be scanned by adjusting theelectron beam direction via the scan coils 123. For example, byoperating scan coils 123, incident beam 112 may be shifted or scanned(as shown with dashed lines) to focus onto different locations of sample14. The sample 14 may be thin enough to not impede transmission of mostof the electrons in the electron beam 11.

Primary axis 110 of the imaging system may be the central axis of theelectron beam emitted from the electron source 10. Primary axis 110 mayalso be the central axis of the condenser lens 121. When the incidentbeam is not shifted or scanned (that is, incident beam 112), theincident beam may be focused at the location where the primary axis 110intersects the sample 14.

The sample 14 may be held by a sample holder 13. The sample holder 13may adjust sample orientation by tilting and/or translating the sample.As an example, FIG. 4 illustrates the coordination system for adjustingthe sample orientation. In FIG. 4, the incident beam 112 may be focusedon sample 14 along the primary axis 110 of the imaging system. Thez-axis may be parallel to the primary axis 110. The x-y plane may be aplane perpendicular to the z-axis. The sample 14 may be tilted relativeto the primary axis 110 by rotating around the x-axis or around they-axis. For example, the rotation direction around the x-axis may be thealpha tilt direction 1001, and the rotation direction around the y-axismay be the beta tilt direction 1002. The sample holder may alsotranslate or shift the sample 14 along any of the x-axis, y-axis, andz-axis. In some embodiments, the sample 14 may be rotated around thet-axis.

Turning back to FIG. 1, electrons 101 passing through sample 14 mayenter projector 116. In one embodiment, the projector 116 may be aseparate part from the focusing column. In another embodiment, theprojector 116 may be an extension of the lens field from a lens infocusing column 12.

The projector 116 may be adjusted by the controller 30 so that directelectrons passed through the sample, impinge on disk-shaped bright fielddetector 115, while diffracted or scattered electrons, which were morestrongly deflected by the sample, are detected by one or more of ahigh-angle annular dark-field (HAADF) detector 18 and a annulardark-field (ADF) detector 19. Signals from the HAADF and ADF detectorsmay be amplified by amplifier 20 and amplifier 21, respectively. Signalsfrom bright field detector 115 may be amplified by amplifier 22. Signalsfrom the amplifiers 20, 21, and 22 may be sent to image processor 24,which can form an image of sample 14 from the detected electrons. TheHAADF detector 18, ADF detector 19, and bright field detector 115 may bea scintillator-photomultiplier detector, a solid-state PIN detector, ora metal plate. The STEM system 100 may simultaneously detect signalsfrom one or more of the ADF detector, the ADF detector, and the HAADFdetector.

The zone axis of the sample 14 may be aligned with the incident beam 112based on diffraction patterns of the sample 14 acquired when irradiatingthe sample with incident beam 112. In one embodiment, the diffractionpattern may be acquired via camera 142 by capturing the diffractionpattern formed on the flu-screen 141. The flu-screen 141 may be insertedbetween the projector 116 and the bright field detector 115 during zoneaxis alignment. For example, the flu-screen 141 may be positionedbetween the HAADF detector 18 and ADF detector 19. The HAADF detectormay be retracted for acquiring the diffraction pattern. In anotherembodiment, the diffraction pattern on the flu-screen may be capturedvia camera 143 positioned downstream of the bright field detector 115.The camera 143 may be CCD or CMOS camera. In some embodiments, thediffraction pattern may be acquired by a pixelated detector. Thepixelated detector may also be used for detecting one or more of thebright field, ADF, and HAADF images. The acquired diffraction patternsmay be sent to the controller 30 for determining the zone axis tilt.

The controller 30 may control the operation of the imaging system 100,either manually in response to operator instructions or automatically inaccordance with computer readable instructions stored in non-transitorymemory 32. The controller 30 can be configured to execute the computerreadable instructions and control various components of the imagingsystem 100 in order to implement any of the methods described herein.For example, the controller may adjust the beam location on the sampleby operating the scan coils 123. The controller may adjust the profileof the incident beam by adjusting one or more apertures and/or lens inthe focusing column 12. The controller may adjust the sample orientationrelative to the incident beam by tilting the sample holder 13. Thecontroller may shift the sample relative to the incident beam bytranslating the sample holder 13. The controller 30 may further becoupled to a display 31 to display notifications and/or images of thesample. The controller 30 may receive user inputs from user input device33. The user input device 33 may include keyboard, mouse, ortouchscreen.

Though a STEM system is described by way of example, it should beunderstood that the present techniques may be used for zone axisalignment with collimated incident beam. The present techniques may alsobe useful when applied to sample alignment in other charged particlebeam microscopy systems, such as transmission electron microscopy (TEM)system, scanning electron microscopy (SEM) system, and dual beammicroscopy system. The present discussion of STEM imaging is providedmerely as an example of one suitable imaging modality.

FIG. 3 shows method 300 for aligning zone axis of a sample with theincident beam in an imaging system such as the STEM system of FIG. 1.The sample may be curved or bendy. In one example, the sample has acurvature equal or greater than 0.5 degree in an area of 2 um². Inanother example, the sample has a curvature equal or greater than 5degrees in an area of 2 um². The zone axis at a particular samplelocation may be automatically aligned with the incident beam (such asprimary axis 110 of FIG. 1) based on the convergent beam diffractionpattern.

At 302, conditions of the imaging system are checked. Checking systemconditions may include checking one or more of whether the system isoperational, whether a suitable sample is inserted, and whether thedesired system settings are in place. An operational system may include,but is not limited to, open columns valves, working electron source, andfunctional system vacuum. The system settings include, but are notlimited to, desired aperture size, condenser lens current, beamposition, camera length, electron potential, and beam current. Step 302also includes acquiring a low-resolution large field of view (FOV)sample image. The sample image may be a STEM image. The sample image maybe used for locating the region of interest (ROI) for high resolutionimaging. For example, the large FOV STEM image is acquired at 5,000×magnification.

At 304, an initial sample image of the ROI is acquired. The initialsample image has a higher resolution and smaller FOV than the sampleimage acquired at 302. The initial sample image may be acquired by theHAADF detector.

At 306, a diffraction pattern of the sample is acquired. The diffractionpattern may be acquired by focusing the charged particle beam at a pointwithin the ROI. As an example, the diffraction pattern is acquired byfocusing the charged particle beam at the center of the ROI andreceiving the charged particles with the detector in the transmissionmode. The HAADF detector is retracted during the diffraction patternacquisition. The diffraction pattern may be acquired with camera 143 ofFIG. 1.

At 308, the trained network receives the diffraction pattern, andoutputs two segmented areas of the diffraction pattern. FIG. 5A shows anexample diffraction pattern formed with a convergent charged particlebeam. The direct beam 503 is a bright circle resulting from the incidentbeam transmitted through the sample directly hitting the detectorwithout scattering. The diffraction spots 504 of the Laue circle areoverlapped with the direct beam. FIG. 5B shows the output of the trainednetwork. The output of the trained network is an image with the samesize (or pixels) as the diffraction pattern. The output image includestwo segmented areas 501 and 502. The first segmented area 501corresponds to the Laue circle, and the second segmented area 502corresponds to the direct beam.

At 310, a quality factor representing the quality of the trained networkoutput (or the segmentation quality of the trained network) is comparedwith a threshold quality factor. In one example, the quality factor isdetermined based on the shape of the segmented area corresponding to theLaue circle. The quality factor is higher if the shape of segmented Lauecircle in the diffraction pattern is closer to a circle. In anotherexample, the quality factor is low if the two segmented areas arenon-disjoint. In yet another example, the quality factor may bedetermined based on a degree of overlap between the two segmented areas.The quality factor is higher if the degree of overlap is higher. If thequality factor is greater than a predetermined threshold quality factor,method 300 proceeds to step 310, otherwise, the operator may be notifiedat 322.

At 326, the diffraction pattern may optionally be used for updatingparameters of the trained network at 326. Step 326 also includesdetermining the validity of the diffraction pattern. An invaliddiffraction pattern may result from non-crystalline materials, in whichcase zone axis alignment is not possible. If the diffraction pattern isvalid, the diffraction pattern can be annotated and utilized to updateand re-train the trained network.

At 312, the zone axis tilt is determined based on the network output.The zone axis tilt includes tilt angles relative to two orthogonal axes(such as the alpha tilt and the beta tilt relative to x-axis and y-axisof FIG. 4). The zone axis tilt may be determined based on the locationsof the centers of the two segmented areas in the trained network output.In one example, the center of each segmented area may be the geometriccenter of the segmented area. In another example, one or more of thesegmented areas may be fitted with a circle. The center of the segmentedarea is the center of the fitted circle. In yet another example, onlythe area corresponding to Laue circle, but not the area corresponding tothe direct beam is segmented using the trained network. The direct beamlocation may be in a fixed known location, in which case only thesegmented area of the Laue circle is necessary.

FIG. 5C shows center 505 of segmented direct beam 502, and the center506 of segmented Laue circle 501 of the network output FIG. 5B. Thecenters are shown with respect to the diffraction pattern FIG. 5A. Thedistance 507 between the centers of the two segmented areas increaseswith the degree of misalignment between the zone axis and the incidentbeam axis. The zone axis tilt increases with the distance 507. Thedistance 507 on each of the two orthogonal axes (such as x-axis andy-axis of FIG. 4) increases with the increased zone axis tilt anglearound the axis.

At 314, the zone axis tilt determined at 312 is compared with athreshold zone axis tilt to assess the degree of misalignment. In oneexample, the zone axis tilt in alpha and beta directions may be comparedwith the threshold alpha and beta zone tilt threshold, respectively. Thealpha and beta zone axis tilt threshold may be 0.5 degree. If the zoneaxis tilt is smaller than the threshold zone axis tilt, the zone axis isaligned, and method 300 proceeds to 324 to acquire the high-resolutionsample image. Otherwise, the sample is tilted at 316 to align the zoneaxis of sample with the incident beam.

In another example, instead of the zone axis tilt, the degree of overlapbetween the direct beam and Laue circle is compared with a thresholdoverlap to assess the degree of misalignment. The method may proceed to324 responsive to the degree of overlap greater than the thresholdoverlap.

At 316, the sample orientation is adjusted based on the zone axis tilt.For example, the sample is tilted relative to the two orthogonal axes bythe zone axis tilt. Right after the sample tilting, the sample heightand/or the focus of the charged particle beam on the sample surface isadjusted.

FIG. 5D shows the diffraction pattern of the sample after adjustingsample orientation based on the zone axis tilt. The diffraction spots ofthe Laue circle converge to and overlap with the direct beam. Thecenters of the Laue circle and the direct beam also overlap with eachother.

At 318, after tilting the sample, a second sample image is acquired, andthe sample is shifted based on the comparison between the second sampleimage and the initial sample image. The sample is shifted by operatingthe sample holder, such as sample holder 13 of FIG. 1. The second sampleimage is acquired with the same parameter as the initial sample imageacquired at 304. The second image may be acquired by the HAADF detectorafter inserting the HAADF detector into the beam path. The sample isshifted in the x-y plane shown in FIG. 4 based on the displacementbetween the second sample image and the initial sample image. The sampleis shifted to compensate for the sample drift and tilt inducedmechanical shift caused by tilting the sample. Step 318 may be skippedif the system has an eucentric holder in both orthogonal directions andsample drift is minimal. The eucentric holder maintains the eucentricityof the sample and does not cause sample drift/shift during sampletilting.

At 320, a third sample image may optionally be acquired, and the chargedparticle beam is shifted based on the comparison between the thirdsample image and the initial sample image. The beam shift may beachieved by operating the scan coils 123 of FIG. 1. The third sampleimage is acquired with the same parameter as the initial sample imageacquired at 304. The sample is shifted in the x-y plane shown in FIG. 4based on the displacement between the third sample image and the initialsample image. The beam shift can achieve a higher resolution shiftbetween the sample and the incident beam compared to shifting the sampleusing the sample holder at 318. If the sample holder has sufficientshift precision, this step is not required.

If the determined zone axis tilt is less than a threshold zone axis tiltat 314, high resolution image of the sample is acquired at 324. Theresolution of the acquired sample image is higher than the sample imagesacquired for zone axis alignment or sample drift compensation (such assample images acquired at 304, 318, and 320).

In some examples, the trained network may only segment the areacorresponding to the Laue circle. The center of the Laue circle may bedetermined based on the segmented area. The center of the direct beammay be predetermined, such as the center of the diffraction pattern. Thesample may be tilted based on the positions of the Laue circle centerand the direct beam center in the diffraction pattern.

In this way, zone axis of a curved or bendy sample is automaticallyaligned with the incident beam based on the convergent beam diffractionpattern of the sample. Since the convergent beam can have a beam profile(or beam cross-section at the sample surface) in the nanometer scale,crystal orientation at a small selected area may be aligned. The zoneaxis tilt is determined automatically based on the locations of thesegmented direct beam and the segmented Laue circle in the diffractionpattern using the trained network. The quality of the network output isassessed before tilting the sample to ensure the accuracy of the zoneaxis tilt estimation. Though zone axis alignment using a convergent beamdiffraction pattern is provided herein as an example, method 300 canalso achieve automatic zone axis alignment using a parallel beamdiffraction pattern.

FIG. 6 shows a method 600 for training the network with one or morereference samples. The network may comprise a single machine learningnetwork or a plurality of machine learning network working incombination. The individual machine learning networks may correspond toa CAN, an ANN, a GAN, a FCN, a U-NET, a YOLO, a Mask R-CNN, or any othertype of machine learning network capable of image segmentation. Forexample, the network may comprise a fully convolutional neural network.According to the present disclosure, the network is trained withmultiple diffraction patterns of the one or more reference samples, andthe annotated diffraction patterns.

The reference sample may be flat or have a curvature less than thesample imaged in FIG. 3. A flat reference sample ensures that acquireddiffraction patterns represent the specified tilt. Otherwise, due tospecimen bending, the actual tilt may not correspond to the specifiedtilt if the sample shifts on holder tilting. If sample driftcompensation is used, as described in step 608, the need for flatness isreduced. The sample material may or may not be the same material as theimaging sample. The reference sample and the imaging sample arecrystalline of the same lattice type. For example, the reference andimaging samples may be silicon near the 110 zone axis. It does notmatter whether the silicon has been processed by lithography, etching,doping, or other semi-conductor manufacturing processes. Support forother lattice types can be realized by including these lattice typesduring training. The reference sample thickness may be different or thesame as the imaging sample provided the charged particle beam cantransmit through the material.

At 602, the zone axis of the reference sample is aligned with theincident beam axis. The zone axis of the reference sample may be alignedmanually.

At 604, an initial image of the reference sample is acquired. Thereference sample image may be a STEM image for compensating sampledrift.

At 606, the reference sample is tilted by a predetermined step sizewithin a predetermined tilt range, and a diffraction pattern of thetilted reference sample is acquired. For example, the tilt step size maybe 1 degree, and the tilt range may be −5 to 5 degrees in both the alpharotation direction and the beta rotation direction. In another example,the tilt step size may vary based on the total sample tilt angle. Thetilt step size may be reduced at smaller tilt angle. The diffractionpattern may be acquired with the same system configuration or parametersas during zone axis alignment of FIG. 3. For example, the diffractionpattern is acquired with the same beam convergence angle as in 304 ofFIG. 3.

At 608, a second reference sample image is acquired, and the referencesample is shifted by comparing the second reference sample image withthe initial reference sample image. Similar to step 314 of FIG. 3, thesample is shifted in the x-y plane to compensate for the sample driftduring sample tilt at 606. Step 608 may further include shifting theincident beam based on the comparison of the second reference sampleimage and the initial reference sample image.

At 610, method 600 checks whether the complete tilt series is acquired.If acquisition of the tilt series is complete, method moves to 612.Otherwise, the sample is further tilted according of the predeterminedstep size at 606.

At 612, each diffraction pattern in the tilt series is annotated basedon the known zone axis tilt corresponding to the diffraction pattern.Annotating the diffraction pattern includes masking the areascorresponding to the direct beam and the Laue circle in the diffractionpattern. The area corresponding the direct beam may be annotated basedon the known direction beam location in the diffraction pattern and theknown beam size, which is proportional to beam convergence angle. Thearea corresponding to the Laue circle may be annotated based on theknown zone axis tilt angle.

In one example, as shown in the annotated diffraction pattern FIG. 7A,the direct beam and the Laue circle are represented with two circles.Circle 701 corresponds to the direct beam, and circle 702 corresponds tothe Laue circle. The center location and the radius of circle 702 aredetermined based on the known sample zone axis tilt from the incidentbeam and from the beam convergence angle.

Turning back to FIG. 6, at 614, the tilt series and the annotated tiltseries are used for training the network. The network receives thediffraction pattern as input, and outputs two segmented areascorresponding to the direct beam and the Laue circle. FIG. 7B shows anexample output of the network. The output is an image including a firstsegmented area 704 corresponding to the Laue circle and a secondsegmented area 703 corresponding to the direct beam. The parameters ofthe network may be adjusted based on the similarity of the annotateddiffraction pattern FIG. 7A and the network output FIG. 7B. For example,the similarity may be calculated based on cross entropy of the twoimages. The training may be completed until the difference between theannotated diffraction pattern and the network output is lower than apredetermined threshold level or when the difference stops improving.Parameters of the trained network may be saved in the non-transitorymemory of the system for zone axis alignment (method 300 of FIG. 3).

In some examples, the network is trained with multiple referencesamples. The reference samples may have different thickness or differentcrystal orientations. They may be of different materials or latticetypes. In some examples, the network is trained on tilt series collectedat different spots of a reference sample.

In some examples, the Laue circle for network training and zone axisalignment is the zero order Laue circle. In other examples, the Lauecircle may include higher order Laue circle, such as the combination ofthe zero order and first order Laue circles.

In some examples, the network is trained to segment only the areacorresponding to the Laue circle, but not the area corresponding to thedirect beam.

In this way, a trained network for segmenting the Laue circle and thedirect beam is generated based on multiple diffraction patterns of oneor more reference samples. Annotation for each diffraction pattern isgenerated automatically based on the known zone axis tilt angle of thediffraction pattern and known beam convergence angle.

What is claimed is:
 1. A method, comprising: directing a chargedparticle beam towards a sample; acquiring a diffraction pattern of thesample; using a trained network, segmenting the diffraction pattern andgenerating a segmented area of the diffraction pattern corresponding toa Laue circle; and determining a zone axis tilt based on the segmentedarea.
 2. The method of claim 1, further comprising determining a shapeof the segmented area, and determining the zone axis tilt based on thesegmented area responsive to the shape of the segmented area being acircle.
 3. The method of claim 1, further comprising generating a secondsegmented area of the diffraction pattern corresponding to a direct beamusing the trained network; and determining the zone axis tilt basedfurther on the second area.
 4. The method of claim 1, wherein thecharged particle beam is a convergent charged particle beam.
 5. Themethod of claim 1, wherein the charged particle beam is a parallelcharged particle beam.
 6. The method of claim 1, further comprisinggenerating the trained network by training a network with multiplediffraction patterns acquired with the charged particle beam.
 7. Themethod of claim 1, further comprising generating the trained network bytraining a network with multiple diffraction patterns of a secondsample, the second sample with a curvature less than the sample.
 8. Themethod of claim 7, wherein training the network with the multiplediffraction patterns of the second sample includes: acquiring themultiple diffraction patterns of the second sample by tilting the secondsample along two orthogonal axes with known tilt angles; annotating adirect beam and the Laue circle in the multiple diffraction patternsbased on the known tilt angles; and training the network with theacquired multiple diffraction patterns and the multiple annotateddiffraction patterns.
 9. The method of claim 8, wherein training thenetwork with the acquired multiple diffraction patterns and the multipleannotated diffraction patterns includes: inputting the multiplediffraction patterns to the network; and adjusting parameters of thenetwork by comparing output of the network with the multiple annotateddiffraction patterns.
 10. A method, comprising: directing a convergentcharged particle beam towards a sample; acquiring a diffraction patternof the sample; using a trained network, segmenting the diffractionpattern and generating a first segmented area in the diffraction patterncorresponding to a Laue circle and a second segmented area in thediffraction pattern corresponding to a direct beam; and determining azone axis tilt based on the first segmented area and the secondsegmented area.
 11. The method of claim 10, further comprising:acquiring a diffraction pattern tilt series including multiplediffraction patterns; annotating the diffraction pattern tilt series byannotating the direct beam and the Laue circle in each diffractionpattern of the diffraction pattern tilt series; and training the networkusing the acquired diffraction pattern tilt series and the annotateddiffraction pattern tilt series.
 12. The method of claim 11, wherein theannotated direct beam and the annotated Laue circle are circles.
 13. Themethod of claim 10, further comprising determining a quality factorbased on the first area and the second area; and determining a zone axistilt based on the first segmented area and the second segmented areaincludes determining the zone axis tilt responsive to comparing thequality factor with a threshold quality factor.
 14. The method of claim13, determining the quality factor based on the first segmented area andthe second segmented area includes determining the quality factor basedon a degree of overlap between the first segmented area and the secondsegmented area.
 15. The method of claim 13, determining the qualityfactor based on the first segmented area and the second segmented areaincludes determining the quality factor based on a shape of the firstsegmented area.
 16. The method of claim 10, further comprisingresponding to the determined zone axis tilt greater than a thresholdzone axis tilt, acquiring a second diffraction pattern of the sample andtilting the sample with a second zone axis tilt determined based on thesecond diffraction pattern using the trained network.
 17. A system forimaging a sample, comprising: a source for generating a charged particlebeam; a sample holder for tilting the sample; a detector; and acontroller with instructions stored in a non-transitory memory, thecontroller is configured to: direct the charged particle beam towardsthe sample; acquire a diffraction pattern of the sample; using a trainednetwork, segment the diffraction pattern and generate a segmented areaof the diffraction pattern corresponding to a Laue circle; and determinea zone axis tilt based on the segmented area.
 18. The system of claim17, wherein the controller is further configured to: determine a qualityfactor based on the segmented area; and tilt the sample responsive tothe quality factor greater than a threshold quality factor.
 19. Thesystem of claim 17, wherein determine the zone axis tilt based on thesegmented area includes: determine a location of a Laue circle center inthe diffraction pattern based on the segmented area; and determine thezone axis tilt based on the location of the Laue circle center and alocation of a direct beam center in the diffraction pattern.
 20. Thesystem of claim 17, wherein the charged particle beam is a convergentcharged particle beam, and wherein the controller is further configuredto acquire an image of the sample by scanning the convergent chargedparticle beam over the sample after tilting the sample.